Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Bichon, Maxime; Tanou, Julien Le; Ropert, Michael; Hamidouche, Wassim; Morin, Luce

doi:10.1109/tip.2019.2919180

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…Fiengo et al [26] proposed a convex optimization based recursive R-D model, which achieves a gain of 12% in coding efficiency as compared with [10] with a 10x-50x higher encoding time. Ropert et al [27], [28] proposed a sequential two-pass method for a better rate allocation, which results in an increase of 16% in coding efficiency at a cost of an average increase of 57% in encoding time as compared with [1].…”

Section: Other Methodsmentioning

confidence: 99%

A Generalized Rate-Distortion-$λ$ Model Based HEVC Rate Control Algorithm

Tang,

Wen,

Han

2019

Preprint

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The High Efficiency Video Coding (HEVC/H.265) standard doubles the compression efficiency of the widely used H.264/AVC standard. For practical applications, rate control (RC) algorithms for HEVC need to be developed. Based on the R-Q, R-ρ or R-λ models, rate control algorithms aim at encoding a video clip/segment to a target bit rate accurately with high video quality after compression. Among the various models used by HEVC rate control algorithms, the R-λ model performs the best in both coding efficiency and rate control accuracy. However, compared with encoding with a fixed quantization parameter (QP), even the best rate control algorithm [1] still under-performs when comparing the video quality achieved at identical average bit rates.In this paper, we propose a novel generalized rate-distortion-λ (R-D-λ) model for the relationship between rate (R), distortion (D) and the Lagrangian multiplier (λ) in rate-distortion (RD) optimized encoding. In addition to the well designed hierarchical initialization and coefficient update scheme, a new model based rate allocation scheme composed of amortization, smooth window and consistency control is proposed for a better rate allocation. Experimental results implementing the proposed algorithm in the HEVC reference software HM-16.9 show that the proposed rate control algorithm is able to achieve an average of BDBR saving of 6.09%, 3.15% and 4.03% for random access (RA), low delay P (LDP) and low delay B (LDB) configurations respectively as compared with the R-λ model based RC algorithm [1] implemented in HM. The proposed algorithm also outperforms the state-of-the-art algorithms, while rate control accuracy and encoding speed are hardly impacted.

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Section: Other Methodsmentioning

confidence: 99%

A Generalized Rate-Distortion-$λ$ Model Based HEVC Rate Control Algorithm

Tang,

Wen,

Han

2019

Preprint

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“…where cur is POC of current frame, p L0 is the previous L0 frame, & is logic 'And' operation, and operator mod is to get the remainder between the left parameter and the right parameter. The function of mod operation in (38) is to ensure the period of L0 frames be at least 6 GOPs. When a new L0 frame is determined, the corresponding frame is replaced by the new L0 frame, and I F p L0 ,seq of previous L0 frame is used to determine the λ-QP pair for the new L0 frame.…”

Section: Optimizing Sub-frame Layers In L0mentioning

confidence: 99%

“…Li et al [37] used the similar distortion propagation model as [33] to extract CTU layer inter-frame dependency, but they focused on bits dependency rather than distortion dependency. Besides Inter prediction, Skip mode was also considered in Bichons' inter-frame dependency model [38], and better performance was achieved. Different from the above inter-frame dependency model that measured distortion with MSE, human visual compliant features were used to weight distortion in [39]- [41] and better structural similarity (SSIM) [42] gaining was achieved.…”

Section: Introductionmentioning

confidence: 99%

Sub-Frame Layer Rate Distortion Optimization for High Efficiency Video Coding

Wei

Zhou

et al. 2020

IEEE Access

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This paper proposes a sub-frame layer RDO algorithm to further improve coding performance of low-delay hierarchical prediction structure in High Efficiency Video Coding. In the proposed algorithm, each frame is segmented into two sub-frames according to motion intensity, and a sub-frame layer is taken as a basic optimizing unit. An inter-frame dependency model is proposed to quantify inter-frame dependency of a frame or a sub-frame. The proposed inter-frame dependency model is composed of two parts, Laplace distribution based direct inter-frame dependency model and distortion propagation chain. The direct inter-frame dependency model is used to evaluate dependency caused by direct reference, and the distortion propagation chain shows the relationship between direct reference and indirect reference. With quantified inter-frame dependency, Lagrange optimization is then used to solve the optimal λ-QP pair for each sub-frame layer in frame layer 1 (L1) to frame layer 4 (L4). For frame layer 0 (L0), additional L0 frames are adaptively inserted according to inter-frame dependency of the previous L0 frame, and the optimal λ-QP pair for each sub-frame in L0 is solved using Lagrange optimization. When peak signal to noise ratio is used as quality criterion, the proposed algorithm achieves 7.0% and 7.0% bit-rate reduction with lowdelay_P_main (LDP) and lowdelay_main (LDB) configurations respectively; when structural similarity is used as quality criterion, the proposed algorithm achieves 13.3% and 13.1% bit-rate reduction with LDP and LDB configurations respectively. INDEX TERMS High efficiency video coding, hierarchical prediction structure, rate distortion optimization, sub-frame layer.

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“…However, this scheme cannot adaptively and automatically optimize coding configurations according to different semantic distortion metrics of different tasks. Recently, Bichon et al [31] utilizes the psycho-vision guided function to weight the distortion in HEVC, which improves the subjective quality of encoded images. However, the weighting function for semantic coding is difficult to get through simple calculations.…”

Section: Introductionmentioning

confidence: 99%

Task-driven Semantic Coding via Reinforcement Learning

Shi

Chen

2021

Preprint

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Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have been studied for this purpose due to their inherent end-to-end optimization mechanism. However, the traditional hybrid coding framework cannot be optimized in an end-toend manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process. Therefore, it is still attractive and challenging to implement task-driven semantic coding with the traditional hybrid coding framework, which should still be widely used in practical industry for a long time. To solve this challenge, we design semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Instead of directly integrating the semantic fidelity metric into traditional hybrid coding framework, we implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL). We formulate the semantic bit allocation problem as a Markov decision process (MDP) and utilize one RL agent to automatically determine the quantization parameters (QPs) for different coding units (CUs) according to the task-driven semantic fidelity metric. Extensive experiments on different tasks, such as classification, detection and segmentation, have demonstrated the superior performance of our approach by achieving an average bitrate saving of 34.39% to 52.62% over the High Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related semantic fidelity.

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Optimal Adaptive Quantization Based on Temporal Distortion Propagation Model for HEVC

Cited by 14 publications

References 31 publications

A Generalized Rate-Distortion-$λ$ Model Based HEVC Rate Control Algorithm

A Generalized Rate-Distortion-$λ$ Model Based HEVC Rate Control Algorithm

Sub-Frame Layer Rate Distortion Optimization for High Efficiency Video Coding

Task-driven Semantic Coding via Reinforcement Learning

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